Effects of physico‐chemical pre‐treatment on the performance of an upflow anaerobic sludge blanket (UASB) reactor treating textile wastewater: application of full factorial central composite design
Bibliographic record
Abstract
The aim of this work was to study the influence of the pre‐treatment step, influent chemical oxygen demand (COD), and hydraulic retention time (HRT) on the decolourization and COD removal efficiency of the upflow anaerobic sludge blanket (UASB) reactors for treating textile wastewater. Statistical models were formulated based on these three variables to optimize the decolourization and COD removal efficiency in the UASB reactor using a full factorial central composite design. The high correlation coefficients (R 2 = 0.99) and the low p‐values (≤0.0001) reveal that the models and model terms are significant, which can be used to optimize the operational variables in an adequate way for the prediction of response variables. The COD removal efficiency of 70 % and decolourization efficiency of 81 % were observed for real textile wastewater treatment by UASB reactor without pre‐treatment. Whereas for pre‐treated real textile wastewater, these were 95 % and 100 %, respectively. The pre‐treatment using a pre‐investigated composite coagulant (MC + ACH) was vital in the overall treatment efficiency of the UASB reactor. Validation of model predictions for the treatment of synthetic and real textile wastewaters reveals the efficacy of these models for enhancing the decolourization and COD removal efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".